Flexible, cost-effective solution for peer-to-peer, gaming, and application traffic detection and treatment

ABSTRACT

A method and apparatus for detecting peer traffic based on a heuristic model and deep packet inspection is described. A suspect set of peer packets is detected using a heuristic model. From the suspect set of peer packet, a set of verified peer packets is detected using deep packet inspection. The set of verified peer packets is processed according to the peer processing policy, while the non-verified peer packets is processed according a non-peer policy. Furthermore, the statistics are generated from the set of suspect peer packet. These statistics are used to update the heuristic model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. provisional patentapplication No. 60/905,521, entitled “A Flexible, Cost-EffectiveSolution for Peer-To-Peer, Gaming, and Application Traffic Detection &Treatment”, filed Mar. 6, 2007.

BACKGROUND

1. Field

Embodiments of the invention relate to the field of computer networking;and more specifically, to peer-to-peer, gaming, and application trafficdetection and treatment.

2. Background

Peer-to-peer (peer), gaming and application traffic has come to be thelion's share of the data traffic on the Internet. Peer traffic isdefined herein as traffic used, generated, consumed, processed, and/orexchanged by peer-to-peer, gaming and other peer application thatcommunicate this traffic between end stations. Peer traffic typicallycomprises data packets (“packets”) that can include data or controlinformation used by the peer, gaming, and/or other peer applications.Similarly, peer networks are the network used by peer, gaming, and/orother peer applications.

With most networks not engineered for this kind of traffic, peer has theability to impair networks and upset traditional network designphilosophies and principles. Traditional network design is based on theassumption that not every client is transmitting and/or receivingtraffic at an intermittent rate and not a high, sustained rate.Typically, a user sends email or surfs the web, which are bothintermittent uses of the network. However, for example, a savvy user maywant to initiate a movie download which can result in a high demandplaced on the network over a long period of time. As such, serviceproviders typically have an important goal of detecting this traffic andusing customized treatment techniques.

Peer networks are highly distributed network architectures such that allclients in the peer network provide bandwidth, storage space, andcomputing power for the network. Adding a client to the peer networkincreases the capacity of the peer network. By providing the computingresources clients make available content stored on the client to otherdirectly without going through a central server. In a pure peer network,there is no central server and each client publishes lists of availablecontent to the other clients in the peer network. Alternatively, in ahybrid peer network, central servers do not store the available content,but instead typically host information about which clients are part ofthe peer network and what content each client has available.

Typical applications of peer networks is exchanging of content betweenclients. While in one embodiment, content is audio-visual media (music,pictures, movies, sounds, etc.), in alternate embodiments, content canbe other types of entities capable of being transmitted across the peernetwork (documents, software, etc.). Because each piece of content canbe large, continual exchanging of content can put a high strain on theunderlying network supporting the peer network. In addition, peernetworks tend to be ad hoc, meaning peer networks are overlaid on aservice provider's existing network without the service provider'spermission.

Gaming applications allow one gamer at one end station to play anothergamer at another end station. In one embodiment, gaming applicationstypically uses a client-server model where a central server controls thegame between different gamers. In an alternate embodiment, the gamersdirectly transmit information between the gamer's end stations. Otherpeer applications known in the art are voice over Internet Protocol(VOIP), such as SKYPE, etc.

FIG. 1 illustrates one embodiment of a service provider network 100 witha router forwarding traffic between end stations content servers througha network. In FIG. 1, network 100 comprises end stations 102A-C, router104, core network 106, and content servers 108A-B. End stations 102A-Ccouple to router 104 and router couples to content servers 108A-Bthrough core network 106. While in one embodiment, end station 102A-Care home personal computers, in alternate embodiments, end stations canbe the same or different type of machine (e.g. business computer,personal digital assistant, cell phone, game console, handhleld gamesystem, laptop) computer, etc.). End stations can couple to router 104through any one of the means know in the art (e.g., Ethernet, wireless,digital subscriber line (DSL), cable modem, Fiber, etc.). Router 104provides an entry point into core network 106 by forwarding data trafficfrom end stations 102A-C to content servers 108A-B, from content servers108A-B to end stations 102A-C, and data traffic going between endstations 102A-C. While in one embodiment, router 104 is an edge routerthat forwards traffic between the edge network servicing end stations102A-C and core network 106, in alternate embodiments, router 104 can bea router or switch positioned differently in the service provider's edgeand/or core networks.

Core network 106 is the backbone network of the service provider thattypically has high capacity to handle that high volume of traffictraveling through network 100. Content servers 108A-B serve contentand/or control information for peer, gaming, and other applications toend stations 102A-C. In one embodiment, content servers 108A-B host peerservices by keeping information on the peers in the networks (e.g., endstations 102A-C) and responds to requests for information. In thisembodiment, content servers 108A-B keep track of what end stations108A-C has to share with the other end stations and makes thisinformation available to other interested end stations. In an alternateembodiment, the peer network does not use the content servers, butallows for direct communication between the corresponding end stations102A-C.

Because peer networks generate high volumes of traffic, serviceproviders typically try to control the flow of peer traffic. Controllingof peer traffic typically entails detecting the peer traffic and thenapplying a policy on how to treat the traffic. FIG. 2 (Prior Art) is anexemplary flow diagram of method 200 for detecting and processing peertraffic. In FIG. 2, at block 202, method 200 identifies the peertraffic. Typically, there are two basic ways to detect peer traffic:heuristic model analysis and deep packet inspection (DPI).

A heuristic model analysis identifies peer traffic by comparing atraffic pattern to a known peer signature without inspecting thecontents of each packet. For example, volume of traffic above a certainrate from one end station to another may characterize peer traffic.Alternatively, a burst pattern may also signal peer traffic. Heuristicmodels are based on external characteristics of the packets and packetsflow. While in one embodiment, a heuristic model is based on duration ofthe packet flow, in alternate embodiments, the heuristic is based on thesame and/or different characteristics (average packet size, inter packetgap, etc.) For example, and by way of illustration, a movie download mayhave an open connection for hours and/or a VOIP call may have a fixedsize inter-packet gap. The advantage to a heuristic model is thatbecause there is no packet inspection, the computational resourcesneeded are relatively small. While heuristic model analysis typicallyaccurately detects existing peer traffic, heuristics models tend beover-inclusive by detective additional traffic as peer traffic. Thus,some legitimate services may be interrupted or disconnected altogetherbecause of this misidentification.

On the other hand, DPI inspects the contents of each and every packet inthe data traffic to determine if the packet is part of peer traffic.Packets involved in peer traffic have characteristic data contentpatterns of each packet. Peer networks typically use two types ofpackets: control packets and data packets. Controls packet containsinformation to setup and maintain peer content exchanges. Data packetscontain the data being used in the peer transaction. DPI matches thedata content pattern of the control and/or data peer packet with knowndata content patterns. DPI models are very accurate and do not sufferfrom the over-inclusive problems of the heuristics approach.Nevertheless, DPI is very computational intensive when DPI is used onevery packet flowing through a high-throughput network element, such asrouter 104.

At block 204, method 900 processes the packets that are part of theidentified peer traffic. While in one embodiment, method 200 simplydrops these packets, in alternate embodiments, method 200 processes givethe packets differently (transmits at a fixed or reduced transmissionrate, marks the packets as peer, preferential treatment, etc.).

Peer traffic can be a severe problem for a service provider. The commonway to handle this problem is to detect peer traffic and either drop thepeer traffic or rate limit it so as to not disrupt the serviceprovider's network. Nevertheless, the two ways to detect peer traffichave drawbacks. The heuristic model approach identifies too much trafficas peer traffic, whereas DPI is very computationally intensive.

BRIEF SUMMARY

A method and apparatus for detecting peer traffic based on a heuristicmodel and deep packet inspection is described. A suspect set of peerpackets is detected using a heuristic model. From the suspect set ofpeer packets, a set of verified peer packets is detected using deeppacket inspection. The set of verified peer packets is processedaccording to the peer processing policy, while the non-verified peerpackets is processed according to a non-peer policy. Furthermore,statistics are generated from the set of suspect peer packets. Thesestatistics are used to update the heuristic model.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments of the invention may be best understood by referring to thefollowing description and accompanying drawings which illustrate suchembodiments. The numbering scheme for the Figures included herein issuch that the leading number for a given element in a Figure isassociated with the number of the Figure. However, element numbers arethe same for those elements that are the same across different Figures.In the drawings:

FIG. 1 (Prior Art) illustrates one embodiment of router forwardingtraffic between end stations content servers through a network.

FIG. 2 (Prior Art) is an exemplary flow diagram for detecting andprocessing peer traffic.

FIG. 3 is an exemplary flow diagram for detecting and processing peertraffic according to one embodiment of the invention.

FIG. 4 is a block diagram illustrating peer detection and processingsystem according to one embodiment of the system.

FIG. 5 is a block diagram of a network element that includes a peerdetection and processing system according to one embodiment of thesystem.

FIG. 6 is a block diagram illustrating data flows between ingress,egress and DPI cards according to one embodiment of the system.

FIG. 7A is a block diagram illustrating a line card according to oneembodiment of the invention.

FIG. 7B is a block diagram illustrating a DPI card according to oneembodiment of the invention.

FIG. 8 is an exemplary flow diagram for updating the heuristic peerdetection system according to one embodiment of the invention.

FIG. 9 illustrates one embodiment of a service provider network with apeer monitor, heuristic model generator and a router, where the routerforwarding traffic between end stations content servers through anetwork.

DETAILED DESCRIPTION

In the following description, numerous specific details such as networkelement, heuristic model, deep packet inspection, packet processor deeppacket inspection processor, packet, peer packet, processor card, linecard, deep packet inspection card, control card, and interrelationshipsof system components are set forth in order to provide a more thoroughunderstanding of the invention. It will be appreciated, however, by oneskilled in the art that the invention may be practiced without suchspecific details. In other instances, control structures, gate levelcircuits and full software instruction sequences have not been shown indetail in order not to obscure the invention. Those of ordinary skill inthe art, with the included descriptions, will be able to implementappropriate functionality without undue experimentation.

References in the specification to “one embodiment”, “an embodiment”,“all example embodiment”, etc., indicate that the embodiment describedmay include a particular feature, structure, or characteristic but everyembodiment may not necessarily include the particular feature,stricture, or characteristic. Moreover, such phrases are not necessarilyreferring to the same embodiment. Further, when a particular feature,structure, or characteristic is described in connection with anembodiment, it is submitted that it is within the knowledge of oneskilled in the art to affect such feature, structure, or characteristicin connection with other embodiments whether or not explicitlydescribed.

In the following description and claims, the term “coupled,” along withits derivatives, is used. “Coupled” may mean that two or more elementsare in direct physical or electrical contact. However, “Coupled” mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other.

Exemplary embodiments of the invention will now be described withreference to FIGS. 3-9. In particular, the operations of the flowdiagrams in FIGS. 3 and 8 will be described with reference to theexemplary embodiments of FIGS. 4-7 and 9. However, it should beunderstood that the operations of these flow diagrams can be performedby embodiments of the invention other than those discussed withreference to FIGS. 4-7 and 9 and that the embodiments discussed withreference to FIGS. 4-7 and 9 can perform operations different than thosediscussed with reference to these flow diagrams.

A method and apparatus for detecting peer traffic based on a heuristicmodel and deep packet inspection is described. According to oneembodiment of the invention, peer traffic is detected by heuristic modeldetection and verified with deep packet inspection. Peer traffic isprocessed according to the peer traffic policy, while non-peer trafficis processed according to a non-peer traffic policy. According toanother embodiment of the invention, an ingress line card of a network:element receives the traffic and detects suspect peer traffic. Suspectpeer traffic includes actual peer traffic and non-peer traffic. A deeppacket inspection processing card detects the verified peer traffic inthe suspect card using deep packet inspection. An egress line cardprocesses the verified peer traffic according to the peer trafficpolicy, and processes the non-peer traffic according to the non-peertraffic policy. FIG. 3 is an exemplary flow diagram of method 300 fordetecting and processing peer traffic according to one embodiment of theinvention. In FIG. 3, at block 302, method 300 receives the stream oftraffic. The stream of traffic typically comprises peer and non-peertraffic. In one embodiment, method 300 receives the traffic stream froman ingress port of the network element.

At block 304, method 300 identifies the traffic flow(s). There may oneor more traffic flows in the received stream of traffic. A traffic flowis a stream of packets that are related by one or more similarcharacteristics. A traffic flow may be characterized by one or more ofingress port, egress port, source address, destination address, packettype, virtual local area network (VLAN) tag, multi-protocol labelswitching tag (MPLS), time of day, application type, average packetsize, packet flow duration, length of connection, etc. Traffic flows areused to group streams of packets such that method 300 applies a specifictraffic processing policy the packets in the respective traffic flow. Atraffic flow may have the same or different traffic processing policy asanother traffic flow. A traffic processing policy (“traffic policy”) isa policy used by the network element to process the packets. A trafficpolicy may specify the maximum/minimum transmission rate for the trafficflow, to drop certain packets within the traffic flow, mark certainpackets, enforce traffic shaping parameters, etc. In one embodiment, thetraffic policy may indicate to detect peer traffic and how to processsuch traffic, such as which heuristic model to use to detect suspectpeer traffic. Thus, the traffic policy for the traffic flow can beassociated with a heuristic model common to none, some or all of theother traffic flows.

At block 306, method 300 receives the traffic profiles associated withthe identified traffic flow(s). Method 300 uses these traffic profilesto process the packets in the identified traffic flow(s).

Method 300 further executes a processing loop (blocks 308-324) to detectand process peer traffic. At block 310, method 300 determines if thepacket is identified as a suspect peer packet based on heuristic modelanalysis. Suspect means the packet is initially identified as a peerpacket. However, because heuristic model analysis tends to over-identifypackets as peer packets, the packet identified is not a verified peerpacket. If so, at block 314, method 300 marks this packet for DPIprocessing In one embodiment, method 300 adds a tag or other suitablemarking known in the art indicating the packet is a suspect peer packetrequiring DPI.

At block 314, method 300 determines if the suspect peer packet is averified peer packet using DPI. Heuristic model analysis reduces thenumber of possible peer packets that set DPI. The number reduced dependson the accuracy of the heuristic model and the nature of the traffic inthe traffic flow. Because there are fewer packets requiring DPI, method300 is less computational intensive than peer detection solely based onDPI. While in one embodiment, method 300 detects both peer control anddata packets, in alternate embodiments method detects either control ordata packets. Being able to detect control and data packets separatelyallows a service provider to treat peer control and data exchangeseparately. In one embodiment, DPI detects control packets, but not datapackets, so that a service provider can deny peer content exchanges bydropping the control packets. This is an efficient way to deny peerservices because the bandwidth intensive part of peer, contentexchanges, never starts when the peer control packets are blocked. Inanother embodiment, DPI detects peer data packets so that a serviceprovider may rate limit peer content exchanges to a low enoughtransmission rate to either discourage peer services, keep peer use at apoint that does not disrupt a service provider's network, or otherwisetreat peer traffic.

If the packet is identified as a verified peer packet by DPI, theverified packet is processed as a peer packet at block 318. At block318, method 300 marks the packet for as a verified peer packet. In oneembodiment, method 300 marks the packet as a verified peer packet usinga tag or other suitable marking. At block 318, method 300 processes theverified peer packet according to the traffic profile associated withthe corresponding traffic flow. In one embodiment, method 300 drops thepacket, in alternate embodiments method 300 processes the peer packet inalternate ways (e.g., transmits the packet at a restricted rate, marksthe packet as low priority, etc.). Control passes to block 322 below.

Returning to block 314, if the packet is identified as a non-peerpacket, method 300 processes the packet as a non-peer packet. In oneembodiment, method 300 processes the packet according to the respectivetraffic profile. Control passes to block 322.

At block 322, method 300 periodically updates the heuristic model forinitially detecting peer traffic. While in one embodiment, method 300updates the heuristic model used for all traffic flows, in alternateembodiments, method 300 updates the heuristic model associated with aspecific traffic flow(s). As stated above, the success of the heuristicmodel depends on the model and the type of traffic method 300 isdetecting. By updating the heuristic model used by method 300, in realtime, the success of the heuristic model in accurately detecting peertraffic is increased. While in one embodiment, the update periodicity isbased on the number of peer packet processed (both suspect and peer), inalternate embodiments, the update periodicity is based on a differentmetric (e.g., number of suspect peer packets, number of verified peerpackets, time elapsed, etc.). Updating of the heuristic model is furtherdescribed in FIG. 8 below. At block 324, the processing loop ends.

FIG. 4 is a block diagram illustrating peer detection and processingsystem 400 according to one embodiment of the system. In FIG. 4, system400 comprises configuration module 402, receive module 404, traffic flowdetection module 406, heuristic peer traffic detection module 408,packet processor 410, transmit module 412, DPI module 414, peer packetprocessor 416, and heuristic update module 418. Configuration modules402 couples to traffic flow detection module 408, heuristic peer trafficdetection module 410, transmit module 412, DPI peer traffic detectionmodule 416, and heuristic update module 420. Traffic flow detectionmodule 406 further couples to receive module 404 and heuristic peertraffic detection module 408. Heuristic peer traffic detection module408 further couples to packet processor 410, DPI peer traffic detectionmodule 414 and heuristic update module 418. In addition, DPI peertraffic detection module 414 couples to packet processor 410, heuristicupdate module 418, and peer packet processor 416. Packet 410 and peer416 processors couple to transmit module 412.

Configuration module 402 creates and stores the configuration for thetraffic flow detection 408, heuristic peer traffic detection 410,transmit modules 412, and DPI peer traffic detection 416 modules. In oneembodiment, configuration module creates configuration data Such as thetraffic flow policies, heuristic models and DPI profiles. Configurationmodule 402 further configures traffic flow detection 408, heuristic peertraffic detection 410, transmit modules 412, and DPI peer trafficdetection 416 modules.

Receive module 404 receives the stream of traffic comprising one or moretraffic flows and feeds the stream to the traffic flow detection module406. Traffic flow detection module 406 takes the traffic stream andidentifies the one of more traffic flows in the stream of traffic asdescribed in FIG. 3, block 304. Traffic flow detection module 406further tags the traffic according the to traffic profile associatedwith each traffic flow in the stream of traffic as described in FIG. 3,block 306. In addition, traffic flow detection module 306 forwards theidentified traffic flows to heuristic peer traffic detection module 408.

For each packet in each traffic flow, heuristic peer traffic detectionmodule 408 identifies peer packets according the current heuristicsmodel as described in FIG. 3, block 310 above. Packet(s) identified aspeer are marked as suspect peer packets are further detected by DPIdetection module 414. Packets designated as non-peer packets byheuristic peer traffic detection module 408 are forwarded to packetprocessor for non-peer packet processing as described in FIG. 3, block320. Heuristic peer traffic detection module 408 further feedsstatistics about peer detection to heuristic updated module 418.

DPI peer traffic detection module 414 receives the identified peerpackets for heuristic peer traffic detection module 408 and inspectseach packet to verify if the packets are indeed peer packets asdescribed in FIG. 3, block 314. Each verified peer packet is marked as averified peer packet and forwarded to peer packet processor 410 asdescribed in FIG. 3, block 316. Peer packet processor 410 processes thepeer packets according to the traffic associated with each packet asdescribed in FIG. 3, block 318. Thus, peer packet belonging to differenttraffic flows can be treated differently. While in one embodiment, peerpacket processor 410 drops the peer packets, in alternate embodiments,peer packet processor treats peer packet differently (e.g., transmitsthe packet at a restricted rate, marks the packet as low priority,etc.). If packet processor 416 does not drop the verified peer packets,the verified peer packets are forwarded to transmit module 412, whichtransmits the packets.

Transmit module 412 receives peer packets form peer packet processor 416and non-peer packets from packet processor 410 and transmits thepackets.

FIG. 5 is a block diagram of a network element 500 that includes peerdetection and processing system according to one embodiment of thesystem. While in one embodiment of the invention, backplane 508 iscoupled to line cards 502A-N, DPI cards 504A-B, and processing cards504A-B, other embodiments of the invention describe multiple otherdevices and/or modules coupled to chassis 508. While in one embodiment,peer algorithm is may be part of line cards 502A-N and/or DPI cards504A-B, alternate embodiments may have alternate card arrangements (acombined line and DPI card with one or more ports and a forwardingengine, one processing card per line card, multiple processing card perline card, etc.). Network element 500 includes line cards 502A-N toforward packets.

FIG. 6 is a block diagram of a network element 600 illustrating trafficflows of peer and non-peer traffic between ingress, egress and DPI cardsaccording to one embodiment of the system. In FIG. 6, network element600 comprises a backplane 602 coupled to ingress line cards 604A-C, DPIcard 606, and egress line cards 608A-B. In one embodiment, ingress linecard 104A sends its non-peer traffic 610A to egress line card 608A fortransmission, whereas ingress line cards 6048B-C sends their non-peertraffic 610B-C to egress line card 608B for transmission. In thisembodiment, ingress line cards 604A-C forward the suspect peer traffic612A-C to DPI card 606 for further peer inspection. Ingress line cards604A-B split the traffic stream into peer traffic 662A-C headed for theDPI card for further detection and non-peer traffic 610A-C forwarded tothe e4gress line cards 608A-B for transmission. In one embodimentingress line card 604A detects suspect peer traffic using the heuristicmodel, which is then further inspected by DPI card 606 to determine ifthe packets contained in traffic 612A are indeed peer traffic. Packetsthat are identified by DPI card 606 as being non-peer traffic and peertraffic 614A-B identified as being transmitted are forwarded to egressline cards 608A-B for transmission. Traffic 610A-C identified by ingressline cards 604A-C is forward directly to the respective egress linescards 608A-B for transmission. While in this embodiment, DPI card 606 isa separate card, in alternate embodiment, DPI functionality is part ofsome or all of the ingress 604A-C and/or egress line card 608A-C.Furthermore, in alternate embodiments, ingress line card 604A-C can beegress line cards 608A-C and visa versa, and/or a line card can be bothingress and egress line card for the same traffic flow. In addition,while ill one embodiment, one DPI card perform DPI for some or all ofthe line cards in network element 600, in alternate embodiments,multiple DPI cards perform DPI for one line card, and/or a combinationtherein.

FIG. 7A is a block diagram illustrating a line card 700 according to oneembodiment of the invention. In FIG. 7A, line card 700 comprises port(s)702, port framer(s) 704, packet processor(s) 706, backplane framer(s)708, and backplane connector 710. Point(s) 702 couple to port framer(s)704, which couple to packet processor(s) 706. Packet processor(s) 706couple to backplane framer(s) 708, which couple to backplane connector.

Port(s) 702 are interfaces that couple line card 700 to an externalnetwork. Line card 700 can be either an ingress line card, egress linecard or both. While in one embodiment, port(s) 702 is one or more10/100/1000 Base-T Ethernet points, in alternate embodiments, port(s)can be one or more of the same or different interfaces (e.g.,synchronous optical network (SONET) fiber, asynchronous transfer mode(ATM) interfaces, wireless, Gigabit Ethernet fiber, etc.).

Port framer(s) 704 convert the frames received on port(s) 702 intopackets suitable for packet processor 706 and visa versa. While in oneembodiment, there are two port framers 704, one for ingress traffic andone for egress traffic in alternate embodiments, there are less or moreport framers 704.

Packet processor(s) 706 processes the packets entering or exiting linecard 700. While in one embodiment, packet processor(s) 706 apply aheuristic model to detect suspect peer traffic, in alternate embodimentspacket processor(s) perform additional other functions (e.g. switch orroute traffic, detect traffic flows, apply traffic policy, traffic shapethe packets, etc.). While FIG. 7A illustrates one packet processor,alternate embodiments may have more than one packet processor.

Backplane framer(s) 708 convert the packets received from packetprocessors) 706 into frames suitable for transmission across a backplanevia backplane interface 710 and visa versa. While in one embodiment,there are two backplane framers 708, one for ingress traffic and one foregress traffic, in alternate embodiments, there are less or morebackplane framers 708. Backplane interface 710 is the interface betweenline card 700 and the backplane of the network element.

FIG. 7B is a block diagram illustrating a DPI card 750 according to oneembodiment of the invention. DPI card 750 differs from line card 700 inthat DPI card process packets received form the backplane and not fromsome external source, such as line card 700. DPI card comprises DPIpacket processor(s) 752 coupled to backplane framer(s) 754, whichcouples to backplane interface 756. Backplane framer converts framesreceived form the backplane into packets suitable for DPI by DPI packetprocessor(s) 752 and visa versa. While in one embodiment, DPI card 750has one DPI packet processor 752, in alternate embodiments DPI card 750may have more than one DPI packet processor 752. DPI packet processor(s)perform the DPI to detect peer traffic as described in FIG. 3, block318.

FIG. 8 is an exemplary flow diagram of method 800 for periodicallyupdating the heuristic peer detection system according to one embodimentof the invention. While in one embodiment, the update periodicity isbased on the number of peer packet processed (both suspect and peer), inalternate embodiments, the update periodicity is based on a differentmetric (e.g., number of suspect peer packets, number of verified peerpackets, time elapsed, etc.). In FIG. 8, at block 802, method 300collects peer statistics from heuristic peer identifier, DPI peeridentifier, or both. While one embodiment, method 800 collectsstatistics about average packet size, in alternate embodiments, method800 collects the same and/or different statistics (packet flow duration,connection length, inter-packet gap, etc.).

At block 804, method 300 generates heuristic in formation based on thecollected statistics. As described above, heuristic informationcomprising one or more of: average packet size, packet flow duration,connection length inter-packet gap, etc. At block 808, method 300updates the heuristic models on the line cards performing heuristic peerdetection.

At block 806, method 300 updates the stored heuristics. Using the storedheuristics, method 300 updates the heuristics in the line cardsperforming peer detection.

FIG. 9 illustrates one embodiment of a service provider network 900 witha peer monitor 904, heuristic model generator 906, and router 902, whererouter 902 forwarding traffic between end stations content serversthrough a network. In FIG. 1, network 900 comprises end stations 102A-C,router 902, peer monitor 904, heuristic model generator 906, corenetwork 106, and content servers 108A-B. End stations 102A-C couple topeer monitor 904 and peer monitor 904 couples to heuristic modelgenerator 906 and router 902. End stations 102A-C can couple to peermonitor 904 through any one of the means know in the art (e.g.,Ethernet, wireless, digital subscriber line (DSL), cable modem, fiber,etc.). Heuristic model generator 906 further couples to router 902 androuter 902 further couples to core network 106. Router 902 provides anentry point into core network 106 by forwarding data traffic from endstations 102A-C to content servers 108A-B, from content servers 108A-Bto end stations 102A-C, and data traffic going between end stations102A-C. While in one embodiment, router 902 is an edge router detectsand treats peer traffic as described above with reference to FIG. 3 andforwards traffic between the edge network servicing end stations 102A-Cand core network 106, in alternate embodiments, router 902 can be arouter or switch positioned differently in the service provider's edgeand/or core networks.

In one embodiment, peer monitor 904 monitors peer traffic and capturesstatistics of the peer traffic flowing through peer monitor 904. In analternate embodiment, peer monitors 904 monitors the peer traffics andgenerates statistics as well as treats the peer traffic (e.g., marks thepeer traffic, restrict transmission rate, gives preferential treatment,etc.). Peer monitor 904 forwards the generated statistics to heuristicmodel generator 906. In addition, router 902 can also forward generatedstatistics to the heuristic model generator 906. Heuristic modelgenerator 906 generates heuristic information based on the receivedstatistics as described in FIG. 8, block 804. Furthermore, heuristicmodel generator 906 generates a new and/or updated heuristic model asdescribed above at FIG. 8, block 806 and forwards the new/updatedheuristics model to router 902. Router 902 uses the updated heuristicmodel detect peer traffic as describe in FIG. 3, block 310.

This implementation of the peer traffic detection is an example, and notby way of limitation. Thus, network elements having other architecturalconfigurations can incorporate embodiments of the invention. Examples ofother network elements that could incorporate embodiments of theinvention could have multiple line and/or DPI cards or have a singleline card incorporating the functionality of both the heuristic and DPIpeer detection. Moreover, a network element having the heuristic and/orDPI peer detection functionality distributed across the traffic cardscould incorporate embodiments of the invention.

DPI cards 504A-B as well as line cards 502A-N, and processor cards506A-B included in the different network elements include memories,processors and/or Application Specific Integrated Circuits (ASICs). Suchmemory includes a machine-readable medium on which is stored a set ofinstructions (i.e., software) embodying any one, or all, of themethodologies described herein. Software can reside, completely or atleast partially, within this memory and/or within the processor and/orASICs. For the purposes of this specification, the term“machine-readable medium” shall be taken to include any mechanism thatprovides (e.g., stores) information in a form readable by a machine(e.g., a computer). For example, a machine-readable medium includes readonly memory (ROM); random access memory (RAM); magnetic disk storagemedia; optical storage media; flash memory devices; etc.

ALTERNATIVE EMBODIMENTS

For example, while the flow diagrams in the figures show a particularorder of operations performed by certain embodiments of the invention,it should be understood that such order is exemplary (e.g., alternativeembodiments may perform the operations in a different order, combinecertain operations, overlap certain operations, etc.)

While the invention has been described in terms of several embodiments,those skilled in the art will recognize that the invention is notlimited to the embodiments described, can be practiced with modificationand alteration within the spirit and scope of the appended claims. Thedescription is thus to be regarded as illustrative instead of limiting.

1. A computer method, comprising: receiving a plurality of packets in atraffic flow; processing all of the plurality of packets in the trafficflow in a first stage based on heuristic model analysis to detect aplurality of suspect peer packets in the traffic flow; processing onlythe plurality of suspect peer packets in a second stage based on deeppacket inspection to detect a plurality of verified peer packets in theplurality of suspect peer packets, wherein verified peer control anddata packets are detected separately in the plurality of suspect peerpackets based on the deep packet inspection; processing the plurality ofverified peer packets according to a peer policy, wherein the peerpolicy includes dropping the verified peer control packets to preventsubsequent verified peer data packet exchange and transmitting theverified peer data packets at a restricted rate; and processing packetsof the traffic flow not detected as one of the plurality of verifiedpeer packets according to a non-peer policy.
 2. The computer method ofclaim 1, further comprising: updating the heuristic model based onstatistics associated with the plurality of suspect peer packets.
 3. Thecomputer method of claim 1, further comprising: selecting the trafficflow from a plurality of traffic flows, wherein the selecting is basedon a set of one or more characteristics associated with packets in thetraffic flow, the set of characteristics includes one or more of ingressport, egress port, source address, destination address, packet type,virtual local area network tag, multi-protocol label switch tag,application type, and time of day.
 4. The computer method of claim 1,wherein the heuristic model is based on external characteristics of thetraffic flow, wherein the external characteristics is one of duration ofpacket flow, average packet size, and inter-packet gap.
 5. A computermethod, comprising: performing a first stage packet inspection on aplurality of packets in a traffic flow comprising, detecting a pluralityof suspect peer packets in the plurality of packets, and detecting afirst plurality of non-peer packets in the plurality of packets, whereinthe first stage packet inspection is based on a heuristic modelanalysis; performing a second stage packet inspection on the pluralityof suspect peer packets comprising, detecting a plurality of verifiedpeer packets in the plurality of suspect peer packets, detecting asecond plurality of non-peer packets in the plurality of suspect peerpackets, wherein the second stage packet inspection is based on deeppacket inspection, and separately detecting, based on deep packetinspection, a first subset of the plurality of verified peer packets asa plurality of verified peer control packets and a second subset of theplurality of verified peer packets as a plurality of verified peer datapackets; processing the plurality of verified peer packets according toa peer policy, wherein the peer policy includes dropping the pluralityof verified peer control packets to prevent subsequent verified peerdata packet exchange and transmitting the plurality of verified peerdata packets at a restricted rate; and processing the first plurality ofnon-peer packets and the second plurality of non-peer packets accordingto a non-peer policy.
 6. The computer method of claim 5, furthercomprising: collecting a set of statistics derived from at least theheuristic model analysis, wherein the heuristic model analysis is basedon a heuristic model associated with the traffic flow; generatingheuristic peer traffic detection information based on the set ofstatistics; updating a heuristic model based on the heuristic peertraffic detection information; and detecting additional suspect peerpackets in the traffic flow based on updated heuristic model analysis.7. The computer method of claim 5, wherein the heuristic model is basedon external characteristics of the traffic flow, wherein the externalcharacteristics is one of duration of packet flow, average packet size,and inter-packet gap.
 8. A non-transitory machine-readable medium thatstores instructions, which when executed by a set of one or moreprocessors, cause said set of processors to perform operationscomprising: receiving a plurality of packets in a traffic flow;processing all of the plurality of packets in the traffic flow in afirst stage based on heuristic model analysis to detect a plurality ofsuspect peer packets in the traffic flow; processing only of theplurality of suspect peer packets in a second stage based on deep packetinspection to detect a plurality of verified peer packets in theplurality of suspect peer packets, wherein verified peer control anddata packets are detected separately in the plurality of suspect peerpackets based on the deep packet inspection; processing the plurality ofverified peer packets according to a peer policy, wherein the peerpolicy includes dropping verified peer control packets to preventsubsequent verified peer data packet exchange and transmitting verifiedpeer data packets at a restricted rate, wherein the verified peercontrol and data packets are detected separately in the plurality ofsuspect peer packets based on the deep packet inspection; and processingpackets of the traffic flow not detected as one of the plurality ofverified peer packets according to a non-peer policy.
 9. Thenon-transitory machine-readable medium of claim 8, further comprising:updating the heuristic model based on statistics associated with theplurality of suspect peer packets.
 10. The non-transitorymachine-readable medium of claim 8, further comprising: selecting thetraffic flow from a plurality of traffic flows, wherein the selecting isbased on a set of one or more characteristics associated with packets inthe traffic flow, the set of characteristics includes one or more ofingress port, egress port, source address, destination address, packettype, virtual local area network tag, multi-protocol label switch tag,application type, and time of day.
 11. The non-transitorymachine-readable medium of claim 8, wherein the heuristic model is basedon external characteristics of the traffic flow, wherein the externalcharacteristics is one of duration of packet flow, average packet size,and inter-packet gap.
 12. An apparatus comprising: a peer detectionmodule comprising, a heuristic peer traffic detection module to detect aplurality of suspect peer packets in a traffic flow of data based on aheuristic model; a deep packet inspection traffic detection module todetect a plurality of verified peer packets in the plurality of suspectpeer packets based on deep packet inspection of the plurality of suspectpeer packets; a peer packet processing module to process the pluralityof verified peer packets according to a peer policy, wherein the peerpolicy includes dropping verified peer control packets to preventsubsequent verified peer data packet exchange and transmitting verifiedpeer data packets at a restricted rate, wherein the verified peercontrol and data packets are detected separately by the deep packetinspection traffic detection module and the verified peer control anddata packets are detected in the plurality of suspect peer packets basedon the deep packet inspection; a non-peer packet processing module toprocess packets of the traffic flow not detected as one of the pluralityof verified peer packets according to a non-peer policy; and a set ofone or more physical interfaces through which a packet transmittertransmits the packets of the traffic flow not detected as one of theplurality of verified peer packets.
 13. The apparatus of claim 12,wherein the heuristic peer detection module further comprises: aheuristic update module to update the heuristic model based onstatistics associated with the plurality of suspect peer packets.
 14. Anetwork element comprising: a set of one or more deep packet inspectioncards, each deep packet inspection card comprising a deep packetinspection processor; a set of one or more line cards, each line cardcomprising a packet processor, wherein the packet processor isconfigured to, detect a plurality of suspect peer packets in a trafficflow of data based on a heuristic model, process a plurality of verifiedpeer packets according to a peer policy, the peer policy includesdropping verified peer control packets to prevent subsequent verifiedpeer data packet exchange and transmitting verified peer data packets ata restricted rate, wherein the plurality of verified peer packets aredetected separately as verified peer control packets and verified peerdata packets by the deep packet inspection processor on one of the setof one or more deep packet inspection cards and the plurality ofverified peer packets are detected in the plurality of suspect peerpackets based on deep packet inspection of packets in the plurality ofsuspect peer packets, process packets of the traffic flow not detectedas one of the plurality of verified peer packets according to a non-peerpolicy; and a set of one or more control cards, the control cards tocontrol forwarding of packets between the set of one or more line cards.15. The network element of claim 14, wherein each control card comprisesa heuristic update module to update the heuristic model based onstatistics associated with the plurality of suspect peer packets. 16.The network element of claim 14, wherein the heuristic model is based onexternal characteristics of the traffic flow, wherein the externalcharacteristics is one of duration of packet flow, average packet size,and inter-packet gap.
 17. The computer method of claim 5, furthercomprising selecting the traffic flow from a plurality of traffic flows,wherein the selecting is based on a set of one or more characteristicsassociated with the plurality of packets in the traffic flow, the set ofcharacteristics includes one or more of ingress port, egress port,source address, destination address, packet type, virtual local areanetwork tag, multi-protocol label switch tag, application type, and timeof day.